IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i11p2493-d1399591.html
   My bibliography  Save this article

Modeling and Prediction of Carbon Monoxide during the Start-Up in ICE through VARX Regression

Author

Listed:
  • Alejandro Garcia-Basurto

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Angel Perez-Cruz

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Aurelio Dominguez-Gonzalez

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Juan J. Saucedo-Dorantes

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

Abstract

In a global society that is increasingly interrelated and focused on mobility, carbon monoxide emissions derived from internal combustion vehicles remain the most important factor that must be addressed to improve environmental quality. Certainly, air pollution generated by internal combustion engines threatens human health and the well-being of the planet. In this regard, this paper aims to address the urgent need to understand and face the CO emissions produced by internal combustion vehicles; therefore, this work proposes a mathematical model based on Auto-Regressive Exogenous that predicts the CO percentages produced by an internal combustion engine during its start-up. The main goal is to establish a strategy for diagnosing excessive CO emissions caused by changes in the engine temperature. The proposed CO emissions modeling is evaluated under a real dataset obtained from experiments, and the obtained results make the proposed method suitable for being implemented as a novel diagnosis tool in automotive maintenance programs.

Suggested Citation

  • Alejandro Garcia-Basurto & Angel Perez-Cruz & Aurelio Dominguez-Gonzalez & Juan J. Saucedo-Dorantes, 2024. "Modeling and Prediction of Carbon Monoxide during the Start-Up in ICE through VARX Regression," Energies, MDPI, vol. 17(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2493-:d:1399591
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/11/2493/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/11/2493/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    2. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
    3. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weixiong Wu & Rui Gao & Peng Wu & Chen Yuan & Xiaoling Xia & Renfeng Liu & Yifei Wang, 2024. "Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation," Energies, MDPI, vol. 18(1), pages 1-17, December.
    2. Atanu Ghoshray & Yurena Mendoza & Mercedes Monfort & Javier Ordoñez, 2018. "Re-assessing causality between energy consumption and economic growth," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    3. Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
    4. Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
    5. Bosupeng, Mpho, 2015. "Drivers of Global Carbon Dioxide Emissions: International Evidence," MPRA Paper 77925, University Library of Munich, Germany, revised 2015.
    6. Wu, Tian & Shen, Qu & Xu, Ming & Peng, Tianduo & Ou, Xunmin, 2018. "Development and application of an energy use and CO2 emissions reduction evaluation model for China's online car hailing services," Energy, Elsevier, vol. 154(C), pages 298-307.
    7. Ma, Wen & Fang, Zhuoqiong & Zhang, Xiangfeng, 2023. "Comparative analysis of structural characteristics of China's 18 typical urban agglomerations based on flows of various elements," Ecological Modelling, Elsevier, vol. 479(C).
    8. Chao-Qun Ma & Jiang-Long Liu & Yi-Shuai Ren & Yong Jiang, 2019. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO 2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model," Energies, MDPI, vol. 12(24), pages 1-16, December.
    9. Gerui Li & Yalin Lei & Jianping Ge & Sanmang Wu, 2017. "The Empirical Relationship between Mining Industry Development and Environmental Pollution in China," IJERPH, MDPI, vol. 14(3), pages 1-20, March.
    10. Li, Wenjie & Yang, Lixing & Wang, Li & Zhou, Xuesong & Liu, Ronghui & Gao, Ziyou, 2017. "Eco-reliable path finding in time-variant and stochastic networks," Energy, Elsevier, vol. 121(C), pages 372-387.
    11. Nitin Kumar Singh & Masaaki Nagahara, 2024. "LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses," Energies, MDPI, vol. 17(17), pages 1-32, September.
    12. Lin, Boqiang & Xu, Bin, 2018. "How to promote the growth of new energy industry at different stages?," Energy Policy, Elsevier, vol. 118(C), pages 390-403.
    13. Liu, Jian & Yang, Qingshan & Ou, Suhua & Liu, Jie, 2022. "Factor decomposition and the decoupling effect of carbon emissions in China's manufacturing high-emission subsectors," Energy, Elsevier, vol. 248(C).
    14. Xu, Bin & Lin, Boqiang, 2017. "Factors affecting CO2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model," Energy Policy, Elsevier, vol. 104(C), pages 404-414.
    15. Liying Wang & Junya Wang & Pengxia Shen & Shangqing Liu & Shuwei Zhang, 2023. "Low-Carbon Travel Behavior in Daily Residence and Tourism Destination: Based on TPB-ABC Integrated Model," Sustainability, MDPI, vol. 15(19), pages 1-18, September.
    16. Alshehry, Atef Saad & Belloumi, Mounir, 2017. "Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1339-1347.
    17. Huiling Wang & Jiaxin Luo & Mengtian Zhang & Yue Ling, 2022. "The Impact of Transportation Restructuring on the Intensity of Greenhouse Gas Emissions: Empirical Data from China," IJERPH, MDPI, vol. 19(19), pages 1-16, October.
    18. Dong, Xianzhou & Luo, Yongqiang & Yuan, Shuo & Tian, Zhiyong & Zhang, Limao & Wu, Xiaoying & Liu, Baobing, 2025. "Building electricity load forecasting based on spatiotemporal correlation and electricity consumption behavior information," Applied Energy, Elsevier, vol. 377(PB).
    19. Qianwen Li & Ruyin Long & Hong Chen & Jichao Geng, 2017. "Low Purchase Willingness for Battery Electric Vehicles: Analysis and Simulation Based on the Fault Tree Model," Sustainability, MDPI, vol. 9(5), pages 1-20, May.
    20. Xue-ting Jiang & Min Su & Rongrong Li, 2018. "Investigating the Factors Influencing the Decoupling of Transport-Related Carbon Emissions from Turnover Volume in China," Sustainability, MDPI, vol. 10(9), pages 1-17, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2493-:d:1399591. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.